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UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation
The state-of-the-art models for medical image segmentation are variants of U-Net and fully convolutional networks (FCN). Despite their success, these models have two limitations: (1) their optimal depth is apriori unknown, requiring extensive architecture search or inefficient ensemble of models of...
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| Pubblicato in: | IEEE Trans Med Imaging |
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| Autori principali: | , , , |
| Natura: | Artigo |
| Lingua: | Inglês |
| Pubblicazione: |
2019
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| Soggetti: | |
| Accesso online: | https://ncbi.nlm.nih.gov/pmc/articles/PMC7357299/ https://ncbi.nlm.nih.gov/pubmed/31841402 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1109/TMI.2019.2959609 |
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